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Distinguishing Planting Structures of Different Complexity from UAV Multispectral Images sensors Article Distinguishing Planting Structures of Different Complexity from UAV Multispectral Images Qian Ma 1,2, Wenting Han 1,3,*, Shenjin Huang 3, Shide Dong 4 , Guang Li 3 and Haipeng Chen 3 1 Institute of Soil and Water Conservation, Chinese Academy of Sciences, Ministry of Water Resources, Yangling 712100, China; [email protected] 2 College of Advanced Agricultural Sciences, University of Chinese Academy of Sciences, Beijing 100049, China 3 College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China; [email protected] (S.H.); [email protected] (G.L.); [email protected] (H.C.) 4 Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; [email protected] * Correspondence: [email protected]; Tel.: +86-029-8709-1325 Abstract: This study explores the classification potential of a multispectral classification model for farmland with planting structures of different complexity. Unmanned aerial vehicle (UAV) remote sensing technology is used to obtain multispectral images of three study areas with low-, medium-, and high-complexity planting structures, containing three, five, and eight types of crops, respectively. The feature subsets of three study areas are selected by recursive feature elimination (RFE). Object-oriented random forest (OB-RF) and object-oriented support vector machine (OB-SVM) classification models are established for the three study areas. After training the models with the feature subsets, the classification results are evaluated using a confusion matrix. The OB-RF and OB-SVM models’ classification accuracies are 97.09% and 99.13%, respectively, for the low-complexity planting structure. The equivalent values are 92.61% and 99.08% for the medium-complexity planting Citation: Ma, Q.; Han, W.; Huang, S.; structure and 88.99% and 97.21% for the high-complexity planting structure. For farmland with Dong, S.; Li, G.; Chen, H. fragmentary plots and a high-complexity planting structure, as the planting structure complexity Distinguishing Planting Structures of Different Complexity from UAV changed from low to high, both models’ overall accuracy levels decreased. The overall accuracy of the Multispectral Images. Sensors 2021, OB-RF model decreased by 8.1%, and that of the OB-SVM model only decreased by 1.92%. OB-SVM 21, 1994. https://doi.org/10.3390/ achieves an overall classification accuracy of 97.21%, and a single-crop extraction accuracy of at least s21061994 85.65%. Therefore, UAV multispectral remote sensing can be used for classification applications in highly complex planting structures. Academic Editor: Thomas Jarmer Keywords: UAV; multispectral remote sensing; farmland objects; classification; RF; SVM Received: 17 January 2021 Accepted: 22 February 2021 Published: 12 March 2021 1. Introduction Publisher’s Note: MDPI stays neutral According to statistics published by the United Nations, the world population is with regard to jurisdictional claims in expected to reach about 10 billion in 2050 [1,2]. Population expansion brings new challenges published maps and institutional affil- to the maintenance of food production security. Mastering the area and spatial distribution iations. of regional crops is the prerequisite for accurately obtaining regional crop yields, and the rational allocation of regional water resources. However, smallholders or family farms, which are still prevailing in some developing countries, are responsible for a large share of the world food production. The scattered farmland and discrete crops of smallholders Copyright: © 2021 by the authors. make cropland mapping and monitoring more difficult, affecting the accurate estimation Licensee MDPI, Basel, Switzerland. of regional crop yields and the rational allocation of water resources. The emergence This article is an open access article of remote sensing technology has promoted agricultural production and research, from distributed under the terms and the traditional stage to the stage of refinement, quantification, and mechanism. High- conditions of the Creative Commons Attribution (CC BY) license (https:// quality remote sensing images, especially those of high resolution, can extract feature creativecommons.org/licenses/by/ information from the ground, making the fine classification and monitoring of ground 4.0/). details possible [3,4]. Agricultural information at the farmland scale can be directly applied Sensors 2021, 21, 1994. https://doi.org/10.3390/s21061994 https://www.mdpi.com/journal/sensors Sensors 2021, 21, 1994 2 of 22 to the optimization of cultivation management and the analysis of breeding decisions. It has further applications compared with the large-scale agricultural monitoring technology used for macro decision-making [5]. Moreover, the acquisition of farmland-scale agricultural information places more stringent requirements on image data timeliness and spatial resolution. At present, the monitoring platform for acquiring crop planting information on the farmland scale is mainly based on high-resolution satellite remote sensing and unmanned aerial vehicle (UAV) remote sensing. Compared with satellite remote sensing, UAV remote sensing is less expensive, offers more flexible mobility, and has a short operating cycle, thus providing several advantages in precision agriculture [6–9]. Researchers have focused on the extraction and classification of single and multiple farmland features, based on UAV remote sensing technology and remote sensing recognition algorithms, in recent years. Supervised classification is widely used to achieve high-precision classification results. There are many supervised classifica- tion algorithms, such as the maximum likelihood, single linkage, Mahalanobis distance, support vector machines and random forests. Among them, support vector machines (SVMs) and random forests (RFs) have been widely used in recent years, as they typically offer superior classification [10–13]. Compared with traditional statistical theory, SVMs with simple structures and strong generalization ability can solve a large number of small- sample learning problems [14]. RF uses sample disturbance and attributes disturbance to achieve good robustness in classification convergence and generalization error [15]. Besides this, deep learning, as an extension of the artificial neural network method, is a new exciting and effective classification method. However, its high requirement of samples increases its cost and limits its application in areas lacking samples [16,17]. The extraction of single-crop planting information is mainly realized by appropriate remote sensing recognition algorithms based on the unique phenological characteristics of a crop. At present, the extraction of large-scale crops, such as corn, wheat, rice, to- bacco, and fruit trees, is based on pixel- and object-oriented classification algorithms, and the classification accuracy can reach more than 90% [18–21]. Liu et al. [22] reported a classification accuracy of winter wheat planting information that exceeded 93.0%, using a deep learning algorithm for pixels combined with UAV visible light remote sensing data. Hall et al. [23] constructed spectral and texture features for maize information from UAV multispectral remote sensing data, and applied an object-oriented SVM to realize the extraction of corn planting information with an extraction accuracy of 94.0%. However, in the case of mixed crops or the intercropping of multiple crops, the fine extraction of multi-crop information suffers significant interference. It requires more robust recognition algorithms than single-crop information extraction. Object-oriented image analysis (OBIA) takes the speckle as the primary processing unit. It considers the spectrum, texture, and context as input features, effectively solving the “salt and pepper” phenomenon in pixel classification [24,25]. Scholars have used OBIA to achieve good classification results for complex planting structures containing between three and five different crops [26–29]. For example, based on UAV images, OBIA was used to classify five types of crops with an overall accuracy of 84.7% [30]. Nevertheless, the extraction accuracy of multiple crops is still insufficient because the similarity of two or more crops is high. Wu et al. [31] used a portable UAV to obtain light images, and used an object-oriented classification method to classify rice, lotus root, and vegetables. Their results showed that the mixed classification of lotus root and vegetable land presented a severe challenge, with a relative error as high as 193%. Liu et al. [32] used SVM to distinguish corn, wheat, trees, greenhouses, and other ground features, based on UAV visible images data and digital surface model (DSM) data. Their results indicated that using only spectral features for classification would lead to confusion between trees and corn. In general, the information extraction of single farmland features based on UAV remote sensing data is relatively mature, and the extraction accuracy is high. However, there is still some confusion in the classification of many kinds of crops. In addition, multiple crop classification mainly focuses on three to five different crops, and there have Sensors 2021, 21, x FOR PEER REVIEW 3 of 22 Sensors 2021, 21, 1994 In general, the information extraction of single farmland features based on UAV3 of re- 22 mote sensing data is relatively mature, and the extraction accuracy is high. However, there is still some confusion in the classification
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